Overview

Dataset statistics

Number of variables11
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.1 KiB
Average record size in memory88.1 B

Variable types

NUM11

Reproduction

Analysis started2020-08-25 00:22:45.361809
Analysis finished2020-08-25 00:23:06.693412
Duration21.33 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

oz1 has unique values Unique
oz2 has unique values Unique
oz3 has unique values Unique
oz4 has unique values Unique
oz5 has unique values Unique
oz6 has unique values Unique
oz7 has unique values Unique
oz8 has unique values Unique
oz9 has unique values Unique
oz10 has unique values Unique
target has unique values Unique

Variables

oz1
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.053313293425845e-10
Minimum-2.446849822998047
Maximum2.562549829483032
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:23:06.739201image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.446849823
5-th percentile-1.642515182
Q1-0.7172730416
median-0.01810993813
Q30.7813603729
95-th percentile1.614997482
Maximum2.562549829
Range5.009399652
Interquartile range (IQR)1.498633415

Descriptive statistics

Standard deviation0.999999997
Coefficient of variation (CV)-1417773400
Kurtosis-0.7399375447
Mean-7.053313293e-10
Median Absolute Deviation (MAD)0.7617934942
Skewness-0.02041391703
Sum-7.053313293e-07
Variance0.9999999941
2020-08-25T00:23:06.841617image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.620527684710.1%
 
-0.588530719310.1%
 
1.00524020210.1%
 
0.954627990710.1%
 
-0.684256434410.1%
 
0.977222859910.1%
 
1.09506905110.1%
 
-0.487954437710.1%
 
-0.545076489410.1%
 
-0.980332672610.1%
 
-1.78644716710.1%
 
-2.1353845610.1%
 
1.77112126410.1%
 
1.15329468310.1%
 
-0.897125959410.1%
 
-0.760854065410.1%
 
-0.346376091210.1%
 
1.25908470210.1%
 
-0.568995058510.1%
 
0.215978890710.1%
 
-1.98286247310.1%
 
-0.0386134535110.1%
 
-0.867747783710.1%
 
-1.55984115610.1%
 
1.1145280610.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-2.44684982310.1%
 
-2.42367100710.1%
 
-2.21645045310.1%
 
-2.1499662410.1%
 
-2.14677357710.1%
 
-2.13981294610.1%
 
-2.1353845610.1%
 
-2.1132631310.1%
 
-2.05414652810.1%
 
-2.03226065610.1%
 
ValueCountFrequency (%) 
2.56254982910.1%
 
2.48404240610.1%
 
2.27782058710.1%
 
2.18056702610.1%
 
2.16508388510.1%
 
2.11555600210.1%
 
2.11055302610.1%
 
2.08889794310.1%
 
2.05071663910.1%
 
2.03547334710.1%
 

oz2
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.1624069884419442e-10
Minimum-1.840156316757202
Maximum1.6901582479476929
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:23:06.947971image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.840156317
5-th percentile-1.633841556
Q1-0.8189271092
median-0.001359990682
Q30.8507265896
95-th percentile1.534670126
Maximum1.690158248
Range3.530314565
Interquartile range (IQR)1.669653699

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)-8602838854
Kurtosis-1.135112296
Mean-1.162406988e-10
Median Absolute Deviation (MAD)0.8338763118
Skewness-0.04794667822
Sum-1.162406988e-07
Variance1.000000001
2020-08-25T00:23:07.051798image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.55859315410.1%
 
-1.6224144710.1%
 
-1.00528764710.1%
 
1.21471464610.1%
 
1.04085862610.1%
 
0.594468772410.1%
 
0.94851845510.1%
 
1.68494164910.1%
 
0.458345115210.1%
 
-0.951159834910.1%
 
-0.760435998410.1%
 
1.64977276310.1%
 
1.55992722510.1%
 
0.572931289710.1%
 
0.431971907610.1%
 
-0.325524836810.1%
 
-0.701791822910.1%
 
0.0381270460810.1%
 
-1.02829074910.1%
 
1.44984805610.1%
 
-0.806369006610.1%
 
-1.11631321910.1%
 
-1.06121611610.1%
 
-0.353195548110.1%
 
1.38934588410.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.84015631710.1%
 
-1.83828771110.1%
 
-1.8265781410.1%
 
-1.8234317310.1%
 
-1.82179033810.1%
 
-1.81744039110.1%
 
-1.80942106210.1%
 
-1.80772435710.1%
 
-1.80617177510.1%
 
-1.79833722110.1%
 
ValueCountFrequency (%) 
1.69015824810.1%
 
1.68494164910.1%
 
1.67744231210.1%
 
1.6729861510.1%
 
1.66804969310.1%
 
1.66722357310.1%
 
1.66621208210.1%
 
1.66422045210.1%
 
1.66094970710.1%
 
1.65044212310.1%
 

oz3
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1019874364137649e-09
Minimum-1.7477477788925169
Maximum1.7445122003555298
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:23:07.171112image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.747747779
5-th percentile-1.552652687
Q1-0.8720490038
median0.02203442622
Q30.8510751873
95-th percentile1.57023313
Maximum1.7445122
Range3.492259979
Interquartile range (IQR)1.723124191

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)907451362.4
Kurtosis-1.194572704
Mean1.101987436e-09
Median Absolute Deviation (MAD)0.8691810668
Skewness-0.01915912898
Sum1.101987436e-06
Variance1.000000001
2020-08-25T00:23:07.274590image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.49608981610.1%
 
0.834579408210.1%
 
-0.0602222569310.1%
 
-0.533867180310.1%
 
0.567605972310.1%
 
-0.0701512023810.1%
 
-0.484705328910.1%
 
0.932296812510.1%
 
0.873703658610.1%
 
0.122791029510.1%
 
0.776045560810.1%
 
-1.38504290610.1%
 
-0.565140187710.1%
 
0.992144346210.1%
 
-1.23566794410.1%
 
0.360400229710.1%
 
-0.455748438810.1%
 
-1.12577605210.1%
 
0.631494224110.1%
 
1.65001177810.1%
 
0.830203473610.1%
 
0.57484787710.1%
 
-1.49734759310.1%
 
0.0508203767210.1%
 
0.811172068110.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.74774777910.1%
 
-1.72946083510.1%
 
-1.72646212610.1%
 
-1.71212363210.1%
 
-1.71056783210.1%
 
-1.70922362810.1%
 
-1.70825874810.1%
 
-1.70655524710.1%
 
-1.7061704410.1%
 
-1.69200134310.1%
 
ValueCountFrequency (%) 
1.744512210.1%
 
1.74272692210.1%
 
1.73925435510.1%
 
1.73813259610.1%
 
1.73595488110.1%
 
1.73314762110.1%
 
1.73259079510.1%
 
1.72847890910.1%
 
1.72559297110.1%
 
1.71509206310.1%
 

oz4
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.183951437473297e-10
Minimum-1.750995397567749
Maximum1.6923093795776367
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:23:07.393900image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.750995398
5-th percentile-1.592637026
Q1-0.8640822172
median0.04304082133
Q30.8735452294
95-th percentile1.513635552
Maximum1.69230938
Range3.443304777
Interquartile range (IQR)1.737627447

Descriptive statistics

Standard deviation0.9999999993
Coefficient of variation (CV)4578856389
Kurtosis-1.228868702
Mean2.183951437e-10
Median Absolute Deviation (MAD)0.8655552268
Skewness-0.04439505048
Sum2.183951437e-07
Variance0.9999999986
2020-08-25T00:23:07.497660image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.30122423210.1%
 
-1.65265035610.1%
 
1.51727354510.1%
 
1.12245690810.1%
 
-0.885443031810.1%
 
-0.376107603310.1%
 
1.25525200410.1%
 
-0.792107701310.1%
 
-0.730020642310.1%
 
1.47790288910.1%
 
1.54820978610.1%
 
0.161942809810.1%
 
-1.72008097210.1%
 
0.320644736310.1%
 
1.4557075510.1%
 
0.160321399610.1%
 
0.533830642710.1%
 
1.15756857410.1%
 
-1.58725488210.1%
 
1.32162404110.1%
 
-1.4974008810.1%
 
-0.294270664510.1%
 
-0.219686344310.1%
 
0.242094352810.1%
 
-0.258136272410.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.75099539810.1%
 
-1.74982547810.1%
 
-1.74852430810.1%
 
-1.74427723910.1%
 
-1.74035596810.1%
 
-1.7395250810.1%
 
-1.72938394510.1%
 
-1.72300696410.1%
 
-1.72269320510.1%
 
-1.72062087110.1%
 
ValueCountFrequency (%) 
1.6923093810.1%
 
1.68813407410.1%
 
1.6860195410.1%
 
1.67686605510.1%
 
1.67485415910.1%
 
1.66820621510.1%
 
1.66657519310.1%
 
1.66602611510.1%
 
1.66460716710.1%
 
1.65776574610.1%
 

oz5
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5011755660765402e-10
Minimum-1.7909502983093262
Maximum1.7142390012741089
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:23:07.613595image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.790950298
5-th percentile-1.611753684
Q1-0.8274279088
median0.02870647702
Q30.8657332212
95-th percentile1.525703478
Maximum1.714239001
Range3.5051893
Interquartile range (IQR)1.69316113

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)6661446023
Kurtosis-1.16530104
Mean1.501175566e-10
Median Absolute Deviation (MAD)0.8518537423
Skewness-0.06941125505
Sum1.501175566e-07
Variance1.000000001
2020-08-25T00:23:07.721549image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.22265505810.1%
 
-0.703844666510.1%
 
1.57161210.1%
 
1.52864205810.1%
 
-0.783073604110.1%
 
-0.443849980810.1%
 
0.803379714510.1%
 
0.637363374210.1%
 
1.39697396810.1%
 
-0.820954203610.1%
 
0.4885993610.1%
 
0.78188425310.1%
 
-0.359329491910.1%
 
1.17313432710.1%
 
-0.990251064310.1%
 
-0.316718667710.1%
 
1.57539999510.1%
 
1.61573374310.1%
 
0.580698311310.1%
 
-0.789680957810.1%
 
0.24575951710.1%
 
-0.0906529426610.1%
 
1.25512933710.1%
 
0.0943147540110.1%
 
0.387247383610.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.79095029810.1%
 
-1.78705513510.1%
 
-1.78599715210.1%
 
-1.78319811810.1%
 
-1.77553057710.1%
 
-1.77541530110.1%
 
-1.77183270510.1%
 
-1.76597642910.1%
 
-1.76578545610.1%
 
-1.764750610.1%
 
ValueCountFrequency (%) 
1.71423900110.1%
 
1.71092283710.1%
 
1.70589232410.1%
 
1.70220768510.1%
 
1.69671654710.1%
 
1.69670760610.1%
 
1.68377840510.1%
 
1.68372952910.1%
 
1.67867648610.1%
 
1.67715203810.1%
 

oz6
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.835922926664352e-10
Minimum-1.73227059841156
Maximum1.8098889589309688
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:23:07.837205image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.732270598
5-th percentile-1.562838262
Q1-0.8666049987
median0.03549011052
Q30.8243088424
95-th percentile1.572664303
Maximum1.809888959
Range3.542159557
Interquartile range (IQR)1.690913841

Descriptive statistics

Standard deviation0.9999999995
Coefficient of variation (CV)1131743687
Kurtosis-1.163928533
Mean8.835922927e-10
Median Absolute Deviation (MAD)0.8523602188
Skewness0.005909247146
Sum8.835922927e-07
Variance0.9999999989
2020-08-25T00:23:07.941536image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.619140446210.1%
 
0.770172059510.1%
 
-1.05379021210.1%
 
0.520191371410.1%
 
-0.49544638410.1%
 
0.869507670410.1%
 
-0.0865078344910.1%
 
-0.518233060810.1%
 
-1.45833706910.1%
 
0.740885794210.1%
 
1.73390269310.1%
 
-0.865882515910.1%
 
-0.748691618410.1%
 
1.07550525710.1%
 
-0.103351697310.1%
 
-0.909517228610.1%
 
0.58762377510.1%
 
-0.537746846710.1%
 
0.130519047410.1%
 
1.34892308710.1%
 
-0.482738375710.1%
 
1.29422974610.1%
 
0.0238136965810.1%
 
-0.725236713910.1%
 
-0.973952233810.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.73227059810.1%
 
-1.72036659710.1%
 
-1.71266174310.1%
 
-1.7113580710.1%
 
-1.70112979410.1%
 
-1.69877076110.1%
 
-1.69809019610.1%
 
-1.69471836110.1%
 
-1.69392514210.1%
 
-1.68923473410.1%
 
ValueCountFrequency (%) 
1.80988895910.1%
 
1.80733406510.1%
 
1.80582785610.1%
 
1.80507171210.1%
 
1.79985630510.1%
 
1.79699969310.1%
 
1.79667925810.1%
 
1.79543149510.1%
 
1.79165542110.1%
 
1.77975058610.1%
 

oz7
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.185153663158416e-10
Minimum-1.7031153440475464
Maximum1.7507447004318235
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:23:08.055198image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.703115344
5-th percentile-1.5001257
Q1-0.8846906722
median-0.02236378938
Q30.899836123
95-th percentile1.55822745
Maximum1.7507447
Range3.453860044
Interquartile range (IQR)1.784526795

Descriptive statistics

Standard deviation0.9999999985
Coefficient of variation (CV)1391758681
Kurtosis-1.252697029
Mean7.185153663e-10
Median Absolute Deviation (MAD)0.8868416026
Skewness0.0353601526
Sum7.185153663e-07
Variance0.9999999971
2020-08-25T00:23:08.159686image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.58593106310.1%
 
-0.232583403610.1%
 
-1.23180520510.1%
 
1.18492782110.1%
 
0.774101734210.1%
 
-1.29820275310.1%
 
1.37241911910.1%
 
-0.632308125510.1%
 
-0.595963418510.1%
 
-0.659119188810.1%
 
0.815106213110.1%
 
-1.69661414610.1%
 
1.04426789310.1%
 
0.905148148510.1%
 
-0.405926585210.1%
 
1.48566436810.1%
 
0.267499566110.1%
 
0.287431269910.1%
 
-0.152504518610.1%
 
0.914704918910.1%
 
-1.40753269210.1%
 
0.247718691810.1%
 
0.774077117410.1%
 
-1.27471494710.1%
 
-1.33721065510.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.70311534410.1%
 
-1.69661414610.1%
 
-1.68946874110.1%
 
-1.68934869810.1%
 
-1.68835699610.1%
 
-1.6855386510.1%
 
-1.68316435810.1%
 
-1.67652881110.1%
 
-1.66810560210.1%
 
-1.66807115110.1%
 
ValueCountFrequency (%) 
1.750744710.1%
 
1.74039697610.1%
 
1.7403949510.1%
 
1.73813700710.1%
 
1.73535168210.1%
 
1.73497259610.1%
 
1.72683727710.1%
 
1.72135746510.1%
 
1.7132531410.1%
 
1.70849275610.1%
 

oz8
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.6316771507263184e-09
Minimum-1.7458435297012331
Maximum1.6194801330566406
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:23:08.276098image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.74584353
5-th percentile-1.566684365
Q1-0.8755166233
median0.03902872466
Q30.9315992594
95-th percentile1.463683724
Maximum1.619480133
Range3.365323663
Interquartile range (IQR)1.807115883

Descriptive statistics

Standard deviation1.000000001
Coefficient of variation (CV)-612866338.3
Kurtosis-1.277091182
Mean-1.631677151e-09
Median Absolute Deviation (MAD)0.9118739963
Skewness-0.0663797198
Sum-1.631677151e-06
Variance1.000000001
2020-08-25T00:23:08.381332image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.0673818141210.1%
 
0.692078292410.1%
 
-1.1364352710.1%
 
0.893269002410.1%
 
1.49041390410.1%
 
0.864400029210.1%
 
-1.38027918310.1%
 
0.123620517610.1%
 
-0.532867133610.1%
 
1.4416097410.1%
 
0.742810785810.1%
 
1.32557237110.1%
 
-1.57947695310.1%
 
1.49474644710.1%
 
1.06775152710.1%
 
0.253819793510.1%
 
-1.04560625610.1%
 
0.970575630710.1%
 
0.405605256610.1%
 
-0.42025354510.1%
 
1.31772875810.1%
 
1.25912702110.1%
 
1.45443844810.1%
 
0.40364739310.1%
 
-0.652320146610.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.7458435310.1%
 
-1.74571740610.1%
 
-1.7362067710.1%
 
-1.72744238410.1%
 
-1.72731840610.1%
 
-1.72627067610.1%
 
-1.72497069810.1%
 
-1.72185504410.1%
 
-1.71817719910.1%
 
-1.71520495410.1%
 
ValueCountFrequency (%) 
1.61948013310.1%
 
1.6154476410.1%
 
1.61525130310.1%
 
1.60939860310.1%
 
1.60833108410.1%
 
1.60796475410.1%
 
1.60304081410.1%
 
1.60296344810.1%
 
1.60221540910.1%
 
1.60004925710.1%
 

oz9
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.734029062092304e-10
Minimum-1.7076772451400757
Maximum1.7891278266906738
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:23:08.498379image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.707677245
5-th percentile-1.544352007
Q1-0.8800190538
median-0.008789218962
Q30.8440735489
95-th percentile1.567001814
Maximum1.789127827
Range3.496805072
Interquartile range (IQR)1.724092603

Descriptive statistics

Standard deviation0.9999999998
Coefficient of variation (CV)-2112365570
Kurtosis-1.160051454
Mean-4.734029062e-10
Median Absolute Deviation (MAD)0.8601358831
Skewness0.0297324956
Sum-4.734029062e-07
Variance0.9999999997
2020-08-25T00:23:08.604631image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.0373635739110.1%
 
1.74041485810.1%
 
-0.887432932910.1%
 
-0.0874914228910.1%
 
-0.316761881110.1%
 
-1.41548132910.1%
 
0.24529425810.1%
 
-0.103359587510.1%
 
0.670674860510.1%
 
-0.705777585510.1%
 
-0.67701101310.1%
 
-1.06501543510.1%
 
0.643272936310.1%
 
1.29112529810.1%
 
0.0202853027710.1%
 
0.555372238210.1%
 
1.74738419110.1%
 
-0.309912294110.1%
 
1.22325968710.1%
 
-0.336964607210.1%
 
-1.23181210.1%
 
0.942077219510.1%
 
-0.0570485591910.1%
 
1.06383037610.1%
 
-0.493496447810.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.70767724510.1%
 
-1.70673775710.1%
 
-1.70654988310.1%
 
-1.70555365110.1%
 
-1.70551705410.1%
 
-1.70286977310.1%
 
-1.7010505210.1%
 
-1.69898045110.1%
 
-1.69532978510.1%
 
-1.69434869310.1%
 
ValueCountFrequency (%) 
1.78912782710.1%
 
1.78021085310.1%
 
1.77621161910.1%
 
1.76706957810.1%
 
1.76524198110.1%
 
1.75808787310.1%
 
1.75285065210.1%
 
1.75265729410.1%
 
1.75254058810.1%
 
1.74910891110.1%
 

oz10
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.399253960698843e-09
Minimum-1.7595716714859009
Maximum1.7092487812042236
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:23:08.878828image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.759571671
5-th percentile-1.567751127
Q1-0.8652437627
median0.01058290154
Q30.8716785759
95-th percentile1.558166844
Maximum1.709248781
Range3.468820453
Interquartile range (IQR)1.736922339

Descriptive statistics

Standard deviation0.9999999988
Coefficient of variation (CV)714666548.6
Kurtosis-1.186997593
Mean1.399253961e-09
Median Absolute Deviation (MAD)0.8643809855
Skewness-0.01555505209
Sum1.399253961e-06
Variance0.9999999976
2020-08-25T00:23:08.980790image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.457305699610.1%
 
0.950773596810.1%
 
1.50913202810.1%
 
0.194012105510.1%
 
1.5130319610.1%
 
-1.53646624110.1%
 
-0.412097752110.1%
 
-1.34114146210.1%
 
1.59895110110.1%
 
-0.0522863268910.1%
 
1.10284805310.1%
 
0.212074294710.1%
 
-0.0935857966510.1%
 
1.51299476610.1%
 
0.973405301610.1%
 
-1.73564279110.1%
 
-1.30591392510.1%
 
0.815086007110.1%
 
-1.48563921510.1%
 
-0.998677194110.1%
 
0.162266582310.1%
 
0.0288499984910.1%
 
-1.55343544510.1%
 
1.36062514810.1%
 
1.31608974910.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.75957167110.1%
 
-1.75874388210.1%
 
-1.7557922610.1%
 
-1.74508559710.1%
 
-1.74360799810.1%
 
-1.74239301710.1%
 
-1.74209022510.1%
 
-1.7419252410.1%
 
-1.73564279110.1%
 
-1.73027396210.1%
 
ValueCountFrequency (%) 
1.70924878110.1%
 
1.70546853510.1%
 
1.70375406710.1%
 
1.69524812710.1%
 
1.6849491610.1%
 
1.67886185610.1%
 
1.67799186710.1%
 
1.67750430110.1%
 
1.67088496710.1%
 
1.6706869610.1%
 

target
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0043149805127882e-09
Minimum-2.832218885421753
Maximum2.2488553524017334
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:23:09.094542image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.832218885
5-th percentile-1.802312911
Q1-0.7380957007
median0.1588120311
Q30.7720519304
95-th percentile1.447720957
Maximum2.248855352
Range5.081074238
Interquartile range (IQR)1.510147631

Descriptive statistics

Standard deviation0.9999999997
Coefficient of variation (CV)995703558.2
Kurtosis-0.5451687087
Mean1.004314981e-09
Median Absolute Deviation (MAD)0.7079729587
Skewness-0.4264423281
Sum1.004314981e-06
Variance0.9999999993
2020-08-25T00:23:09.192749image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-2.39061498610.1%
 
-1.42313516110.1%
 
0.712232232110.1%
 
0.300133347510.1%
 
0.789718687510.1%
 
0.199870675810.1%
 
0.0913898199810.1%
 
-0.436847716610.1%
 
0.189615145310.1%
 
0.420244336110.1%
 
0.348394244910.1%
 
-0.754550874210.1%
 
1.45440065910.1%
 
0.226232945910.1%
 
0.651022195810.1%
 
0.412423312710.1%
 
0.764281034510.1%
 
-1.55203771610.1%
 
0.742814660110.1%
 
0.875726103810.1%
 
0.951795935610.1%
 
0.672497928110.1%
 
-0.656872034110.1%
 
-1.97780573410.1%
 
0.96722775710.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-2.83221888510.1%
 
-2.76727557210.1%
 
-2.63240408910.1%
 
-2.54690861710.1%
 
-2.47446107910.1%
 
-2.44212818110.1%
 
-2.41165065810.1%
 
-2.39789295210.1%
 
-2.39061498610.1%
 
-2.37979984310.1%
 
ValueCountFrequency (%) 
2.24885535210.1%
 
2.16936969810.1%
 
2.00538277610.1%
 
1.96646928810.1%
 
1.92469155810.1%
 
1.90444207210.1%
 
1.86196684810.1%
 
1.82967996610.1%
 
1.8276938210.1%
 
1.79036116610.1%
 

Interactions

2020-08-25T00:22:45.856190image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:46.155101image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:46.308866image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:46.461861image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:46.614656image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:46.763720image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:46.913846image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:47.068386image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:47.221537image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:47.373511image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:47.529182image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:47.675676image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:47.833505image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:48.008002image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:48.180127image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:48.346922image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:48.520458image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:48.692481image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:48.862020image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:49.032660image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:49.194475image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:49.359400image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:49.524694image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:49.683025image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:49.844080image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:50.004653image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:50.164177image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:50.324966image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:50.491034image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:50.649341image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:50.968261image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:51.132641image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:51.292129image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:51.449095image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:51.602923image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:51.768391image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:51.929980image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:52.094378image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:52.254281image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:52.414749image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:52.577478image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:52.738797image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:52.897943image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:53.061328image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:53.218831image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:53.374211image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:53.540291image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:53.701910image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:53.863839image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:54.025265image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:54.190047image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:54.350725image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:54.510888image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:54.677235image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:54.840473image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:54.998222image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:55.153940image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:55.326770image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:55.489371image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:55.826010image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:55.987084image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:56.147729image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:56.309615image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:56.472155image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:56.637990image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:56.798443image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:56.954892image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:57.110585image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:57.285790image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:57.522671image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:57.687392image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:57.854941image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:58.015696image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:58.179477image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:58.341097image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:58.500369image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:58.663373image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:58.820242image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:58.975689image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:59.140218image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:59.302423image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:59.463367image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:59.622229image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:59.790928image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:22:59.951961image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:00.111555image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:00.275940image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:00.435964image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:00.758750image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:00.910342image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:01.074911image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:01.235958image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:01.396812image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:01.558223image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:01.722319image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:01.883241image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:02.045506image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:02.207912image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:02.368948image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:02.524516image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:02.677744image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:02.845941image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:03.007502image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:03.168333image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:03.332154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:03.495133image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:03.655393image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:03.818352image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:03.978143image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:04.137932image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:04.300453image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:04.459143image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:04.634969image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:04.796256image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:04.955144image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:05.115149image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:05.273651image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:05.594200image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:05.749658image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:05.907527image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:06.062392image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T00:23:09.313586image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T00:23:09.534261image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T00:23:09.753334image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T00:23:09.976335image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T00:23:06.320472image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:23:06.585634image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

oz1oz2oz3oz4oz5oz6oz7oz8oz9oz10target
01.2533141.4221960.051152-0.5533070.6410220.9766800.6368181.326494-0.1913151.2666320.027008
1-0.2654360.158235-1.1010551.038427-0.1004040.7558421.2966841.1709681.314848-0.2778081.080517
20.5533401.492693-1.1062391.0399041.314257-0.2522221.654212-0.3959130.742190-0.755970-0.577395
30.2571060.1016730.6746550.2631171.4832581.261019-0.813460-0.204744-1.2741571.1028480.291893
4-0.917179-1.2417530.589486-1.031419-0.8271300.572551-0.548512-0.201588-1.0693010.499185-0.565285
50.5484441.602389-1.5492171.352638-1.738407-1.382940-1.318581-1.623646-1.337004-0.450253-0.664754
6-0.024753-0.2944020.3663561.0446280.9765661.7657140.575131-1.363029-1.035936-0.0320491.236952
7-0.2862660.370127-0.5677970.1765170.974586-1.523185-0.2535301.011183-0.930602-0.7845920.299242
8-0.304032-0.022258-0.164777-0.1591161.7109231.553181-0.483317-0.716261-1.5097891.0537050.878490
9-0.543726-1.1029820.9670981.1433731.4012620.0465561.364831-1.047457-0.616102-1.3351581.427699

Last rows

oz1oz2oz3oz4oz5oz6oz7oz8oz9oz10target
9901.7373901.1844321.3420281.0930860.371719-0.719245-0.9422581.2518071.5666570.4695061.275428
991-1.328080-0.802630-0.824151-0.792108-1.264682-0.5889931.0344331.3413821.0253760.271130-0.134193
9921.0323100.8211030.486853-0.148356-1.2687761.260072-0.094002-1.335021-0.019862-0.759503-1.892105
993-1.195180-0.867430-1.2425060.293934-0.4780040.320371-1.549136-1.724971-0.459054-1.4424480.615490
9940.735658-0.2853641.6655020.0875420.387247-1.3134411.735352-1.636060-0.8804920.9450930.640972
995-0.734504-1.3134770.9921441.0382000.1087360.960036-0.670038-0.259006-1.050429-0.9495200.820211
9961.4269280.7205681.6004230.886567-1.648804-1.5328330.5361760.0430421.6982701.051942-0.790412
9970.3500630.789333-0.5460581.305348-1.1601460.230994-0.1597450.3065620.932805-1.568219-1.221568
9980.281373-0.3691661.2846721.170539-0.443850-1.5131561.4412881.2706350.6300301.2684471.089369
999-0.3921780.472205-1.1222381.1443780.053636-1.155011-0.3693581.3422240.8707760.9507740.702655